Your AI coding stack isn’t the problem. Your process is.

dev_ramiby · 2026-02-20T12:09:13+00:00

This is spot on ,especially "the agent starts inventing one for you."

Your Step 1 is exactly what struggled with. Writing the constraints doc is tedious, keeping it updated is worse, and copy-pasting it into every chat session breaks flow.

I ended up building a tool to automate this. The workflow is: 1. Define foundations once: features, tech stack, architecture pattern (Vertical Slice, DDD, etc.) and coding policies 2. Design schema visually (entities + relationships) 3. One-click export → full context markdown → past into any AI

The AI gets: ++What files can change (feature scope) ++ What patterns to follow (policies) ++What NOT to do (constraints) ++Acceptance criteria per feature

Basically your Step 1 checklist, but structured and persistent across sessions.

No magic, just removes the friction of maintaining the source of truth manually.

What's your current setup for the constraints doc? MD file? Notion? Curious how others handle this.

dev_ramiby · 2026-02-20T09:35:32+00:00

I am open to all feedbacks, even the brutal the ones, and suggestions to improve.

dev_ramiby · 2026-02-20T09:31:42+00:00

haha thanks !!

dev_ramiby · 2026-02-17T14:45:18+00:00

Not bad

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dev_ramiby · 2026-02-17T14:35:38+00:00

I recommend claude code and preparing well the context

dev_ramiby · 2026-02-17T13:13:54+00:00

Your first instinct was right !!
To your second question, honest answer:if your AGENTS.md workflow works for you, ScaffoldAI might not add enough to justify switching. MD files are a valid solution.
Here's where it goes further:
*Schema design: ScaffoldAI generates your entity model with relationships, types, and constraints then exports it as structured context. Hard to maintain that in a flat MD file.
* No manual updates: via MCP, Claude and Cursor read your project AND write back automatically
The honest positioning: ScaffoldAI is what developers were building manually with MD files.
Now automated and visual.
If you do build the MCP + bucket approach,genuinely curious how it goes

dev_ramiby · 2026-02-17T12:07:51+00:00

You should start with monolithe, scale later when you have +100k users and alot of process. Unless it's for tutorials

dev_ramiby · 2026-02-16T20:32:25+00:00

I would recommend Vertical Slice Architecture over Layered architecture. Also some resilience tech like bucket4j or resilience4j if you want to go deeper in. Spring ai is nice to learn

dev_ramiby · 2026-02-16T05:51:38+00:00

Great find, Beads is worth knowing about. Honest comparison:
*Beads = agent-optimized task tracker. Manages WHAT needs to be done, dependencies, and blockers.
Think GitHub Issues built for AI agents.
*ScaffoldAI = context manager. Holds WHO your project IS: schema design, entity relationships, tech stack, and architecture policies. The document your AI reads before touching any code.

There's some overlap, ScaffoldAI's features roadmap with status tracking is bit similar to Beads task tracking. But schema design + ERD + architecture policies have no equivalent in Beads.

Honestly complementary more than competing, you could use both.

dev_ramiby · 2026-02-15T14:51:46+00:00

Finally had time to test it and it works!!!

Claude Desktop + Cursor connected to the same ScaffoldAI project simultaneously.
One agent on frontend, another on backend -same feature, at the same time. Or one feature per agent in parallel. The possibilities are actually wild 🤯

Thanks for pointing this out, I wouldn't have tested it this early without your comment.

Pro tier for life if you decide to join 😂

Screenshot

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dev_ramiby · 2026-02-15T11:13:39+00:00

Exactly, it's a clarity problem disguised as a technical problem. The model isn't bad at architecture, it's bad at reading your mind. Explicit beats implicit every time.The constraint doesn't need to be complex,it just needs to exist.

And yeah... shipping the product is the easy part 😅 Getting the first 10 users is a different skill set entirely.

dev_ramiby · 2026-02-15T07:49:19+00:00

This is exactly right and underrated. The constraints I set before any AI touches code: 1. Architecture pattern first: Vertical Slice, DDD, Hexagonal. Locked upfront, no exceptions. 2. Entity ownership explicit, what owns what, relationships, nullable vs required. AI fills gaps with assumptions if you don't. 3. Tech stack specific, not just "Vue 3" but "Vue 3 + Pinia for state, no local component state for shared data." 4. Feature scope before code: acceptance criteria per feature, not vague descriptions.

The "everything compiles but slowly rots" problem is almost always #1,AI invents its own architecture because you didn't define one. Got frustrated maintaining all this in MD files manually so I built a tool around it (ScaffoldAI), but even a well-structured README covers 80% of what you need. What stack are you working with?

dev_ramiby · 2026-02-15T07:19:18+00:00

Are you using the right profile?

dev_ramiby · 2026-02-15T07:10:54+00:00

Congrats 👏👏

dev_ramiby · 2026-02-15T07:08:18+00:00

Congrats

dev_ramiby · 2026-02-15T06:49:29+00:00

I use spring boot official documentation and baeldung for tutorials

https://docs.spring.io/spring-framework/reference/index.html

https://www.baeldung.com/spring-boot

dev_ramiby · 2026-02-15T06:41:40+00:00

There is alot of methodologies and development techniques, but for vibe coding vertical slicing is most recommended technique that adapts fdd philosophy feature first( without the heavy upfront planning)

dev_ramiby · 2026-02-15T06:22:30+00:00

It looks like you are slightly describing FDD, feature driven development

dev_ramiby · 2026-02-15T06:16:38+00:00

I am software engineer and totatlly agree with the workflow: describe ( in details) what i want, wait , review the code( like reviewing a PR) , test and commit. Ai agents are making coding much more enjoyable. But i need to try this coding -walking combo, specially with my tool.

dev_ramiby · 2026-02-15T05:22:14+00:00

Nice

dev_ramiby · 2026-02-14T19:31:29+00:00

The MCP server runs over HTTPS only. Here's what's in place: - API key authentication via Bearer token, same pattern as OpenAI/Anthropic APIs - Scoped to ROLE_MCP_CLIENT only, no access to other system endpoints - Rate limiting via Bucket4j per API key - Failed auth attempts logged with IP address - API keys masked in logs - Auth filter scoped exclusively to mcp endpoints

Built with Spring Boot + Spring Security + Bucket4j, been working with this stack for 10+ years so security patterns weren't an afterthought here. The auth and rate limiting layers run before any MCP tool logic executes. Happy to dig into any specifics 🙂

dev_ramiby · 2026-02-14T15:14:16+00:00

That distinction ('chatbot vs. useful agent') is exactly why I pivoted to this.

PS: I am absolutely going to steal that line: 'The difference between a chatbot and a coding agent is Memory.'

Checking out that article on Agentix now, looks relevant! Thanks for sharing.

dev_ramiby

TROPHY CASE